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An Initial Risk Assessment for Multimodal with LSTM-Based Trust Evaluation Framework for Autonomous Vehicle Security

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SN computer science 2025年第2期6卷 1-15页

作者： Renjith, P.N. Balasubramani, S. Ramesh, K. Patnala, Eswar School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

This research aims to develop a new approach to increase the safety and reliability of Autonomous Vehicle (AV) through the proposed risk assessment framework, supported by the trust evaluation approach derived from a machine learning algorithm. This work proposes a comprehensive risk assessment model with real-time sensor data, Vehicle-to-Vehicle (V2V), key communication reliability, environmental cognition, and Improved Long Short-Term Memory (I-LSTM). This framework is multidimensional compared to most conventional models, focusing primarily on assessing individual threat factors. Here, the machine learning portion is designed to evaluate and predict trust by quickly processing vast amounts of information from sensors, signals, and prior operation history. Such an approach to trust assessment enables the system to continuously evaluate the dependability of automated vehicle operation and identify threats to security with improved accuracy. The effectiveness of the performance of this method is substantiated by a number of stand-alone and live scenarios, where the model's performance is higher in perceiving possible risks, avoiding threats, and enhancing safety than the conventional approaches. The findings suggest that the safety regulations of the AVs will be improved by using a multi-dimensional risk assessment model and integrating the I-LSTM trust rating with 95.5% accuracy, 96.7% risk identification, and 180 ms response time. This will cause AV and its surrounding environment to be accepted by the public and used on the roads. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autonomous vehicles LSTM model Machine learning Risk assessment Trust evaluation

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DyPSS: Dynamic Priority Signalling System for Emergency Vehicles using Fog and YOLOv8 17

DyPSS: Dynamic Priority Signalling System for Emergency Vehi...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Kakde, Anuj Nath, Shubha Brata Ghosh, Soumya K. Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331531195

Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dynamic Priority Signalling System (DyPSS), an intelligent, multi-layered traffic management solution designed to minimize ERV delays by creating "green corridors"at traffic signals. Using technologies like fog computing, cloud computing, global positioning system and deep learning, DyPSS adjusts traffic lights instantly to clear congestion as an ERV approaches. Simulation testing was conducted using SUMO and it demonstrated DyPSS's potential to significantly reduce ERV travel time in dense urban settings with minimal disruption to general traffic flow. This system could drastically improve emergency response by reducing delays and helping responders reach their destinations faster, potentially saving more lives. © 2025 IEEE.

关键词： Emergency vehicles

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AniSan@NLU of Devanagari Script Languages 2025: Optimizing Language Identification with Ensemble Learning 1

AniSan@NLU of Devanagari Script Languages 2025: Optimizing L...

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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Shanto, Anik Mahmud Priya, Sanjida Jamal Arefin, Mohammad Shamsul Department of Computer Science and Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (纸本)9798891762015

Identifying languages written in Devanagari script, including Hindi, Marathi, Nepali, Bhojpuri, and Sanskrit, is essential in multilingual contexts but challenging due to the high overlap between these languages. To address this, a shared task on "Devanagari Script Language Identification" has been organized, with a dataset available for subtask A to test language identification models. This paper introduces an ensemble-based approach that combines mBERT, XLM-R, and IndicBERT models through majority voting to improve language identification accuracy across these languages. Our ensemble model has achieved an impressive accuracy of 99.68%, outperforming individual models by capturing a broader range of language features and reducing model biases that often arise from closely related linguistic patterns. Additionally, we have fine-tuned other transformer models as part of a comparative analysis, providing further validation of the ensemble’s effectiveness. The results highlight the ensemble model’s ability in distinguishing similar languages within the Devanagari script, offering a promising approach for accurate language identification in complex multilingual contexts. © 2025 International Committee on Computational Linguistics.

关键词： Computational linguistics

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CUET_HateShield@NLU of Devanagari Script Languages 2025: Transformer-Based Hate Speech Detection in Devanagari Script Languages 1

CUET_HateShield@NLU of Devanagari Script Languages 2025: Tra...

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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Aodhora, Sumaiya Rahman Ahsan, Shawly Hoque, Mohammed Moshiul Department of Computer Science and Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (纸本)9798891762015

Social networks have become essential platforms for information exchange and free expression. However, their open nature also facilitates the spread of harmful content, such as hate speech, cyberbullying, and offensive language, which pose significant risks to social well-being. This study focuses on developing an automated system to detect hate speech in Devanagari script languages, enabling efficient moderation and timely intervention. Our approach leverages a fine-tuned transformer model for classifying offensive content. We experimented with various machine learning (ML) techniques, including Logistic Regression (LR), Support Vector Machines (SVM), and Random Forest (RF), as well as deep learning (DL) architectures such as CNN, BiLSTM, and CNN-BiLSTM. Additionally, we evaluated transformer-based models, including IndicBERT, m-BERT, MuRIL, Indic-SBERT, and XLM-R. Among these, the fine-tuned XLM-R model delivered the best performance, achieving a macro f1-score of 0.74, demonstrating its effectiveness in detecting hate speech in Devanagari script languages. However, the model submitted for the shared task achieved a macro f1-score of 0.73, ranking 13th in the subtask. © 2025 International Committee on Computational Linguistics.

关键词： Decision trees

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Performance Evaluation of EfficientNet Models in Grape Leaf Disease Classification 1

Performance Evaluation of EfficientNet Models in Grape Leaf ...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Subramanya, S.G. Parkavi, A. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

Grape farming is a globally significant agricultural practice, but grapevines frequently encounter viral, fungal, and bacterial infections that compromise crop quality and yield. Conventional disease detection methods are labor-intensive and require significant expertise. This study investigates deep learning techniques, focusing on EfficientNet models, to automate grape leaf disease identification and classification. The transfer learning approach was applied to train and fine-tune EfficientNet models on a publicly available dataset with four classes: black rot, leaf blight, esca, and healthy leaves. A model performance was measured through accuracy and loss metrics. EfficientNetB0 achieved accuracy of 92.45% and 96.73%, and loss of 0.197 and 0.098, on training and validation data, respectively. However, EfficientNetB5 and EfficientNetB7 gave validation accuracies of 96.07% and 96.39%, respectively. The accuracy and loss trends demonstrate that EfficientNetB0 outperforms the other two counterparts. This approach highlights the potential of EfficientNetB0 for accurate, efficient grape disease detection and classification. © 2025 IEEE.

关键词： Plant diseases

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Deep Learning Utility for Gas/Odor Sensor Signature Analysis 2

Deep Learning Utility for Gas/Odor Sensor Signature Analysis

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Raut, Yashasvi Chaudhri, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra India

ISBN: (纸本)9798331505745

Gas/odor sensors are integral components of sensor systems used in diverse applications such as food quality control, environmental monitoring, medical diagnostics, odor profiling, industrial safety, agriculture, explosives detection, wine tasting, pharmaceutical quality control, and toxic gas detection. Traditional approaches in this domain of gas/odor sensing technologies face challenges related to accuracy, cross-sensitivity, and drifted real-time performance. The integration of deep learning techniques with gas sensors has shown promise in overcoming these limitations. Moreover, this amalgamation of gas sensing and deep learning strategies enables accurate identification of gases/odors, handling complex patterns, and leading optimal consumption of resources. In this paper, an overview of advancements in deep learning-enabled gas sensing has been presented that focused on methodologies, sensor types, and applications. This review also highlights the state-of-the-art techniques and identifies gaps for future directions for research. © 2025 IEEE.

关键词： Damage detection

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CUET_Big_O@NLU of Devanagari Script Languages 2025: Identifying Script Language and Detecting Hate Speech Using Deep Learning and Transformer Model 1

CUET_Big_O@NLU of Devanagari Script Languages 2025: Identify...

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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Hossan, Md. Refaj Sakib, Nazmus Hossain, Md. Alam Miah Jawad Hoque, Mohammed Moshiul Department of Computer Science and Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (纸本)9798891762015

Text-based hate speech has been prevalent and is usually used to incite hostility and violence. Detecting this content becomes imperative, yet the task is challenging, particularly for low-resource languages in the Devanagari script, which must have the extensive labeled datasets required for effective machine learning. To address this, a shared task has been organized for identifying hate speech targets in Devanagari-script text. The task involves classifying targets such as individuals, organizations, and communities and identifying different languages within the script. We have explored several machine learning methods such as LR, SVM, MNB, and Random Forest, deep learning models using CNN, BiLSTM, GRU, CNN+BiLSTM, and transformer-based models like Indic-BERT, m-BERT, Verta-BERT, XLM-R, and MuRIL. The CNN with BiLSTM yielded the best performance (F1-score of 0.9941), placing the team 13th in the competition for script identification. Furthermore, the fine-tuned MuRIL-BERT model resulted in an F1 score of 0.6832, ranking us 4th for detecting hate speech targets. © 2025 International Committee on Computational Linguistics.

关键词： Decision trees

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A Review on the Use of Smart Agriculture Systems to Protect Crops 3

A Review on the Use of Smart Agriculture Systems to Protect ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, Gulshan Gourshettiwar, Palash Suman, Saurabh Kumar, Shravan Kumar, Dhiraj Raushan, Rahul Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331509675

Smart agriculture systems leverage the possibilities offered by cutting-edge technologies such as IoT, AI, and remote sensing to revolutionize conventional farming by enhancing resource utilization, production, and crop damage mitigation. Real-time monitoring of soil and crop health, predictive analytics, pest control, and precision irrigation measures are all enabled by these systems. They are able to address major Indian agriculture issues, consequently boosting yield and profitability and promoting environmental sustainability. The largescale deployment of intelligent agriculture systems will change the agriculture landscape in India and will assure long-term food security for an ever-growing population. Challenges include adequate research and future studies in order to better install and achieve smart agricultural systems to protect crops. Intelligent agriculture involves all advanced research, including science and innovations, in national development through space technologies to enhance soil quality, conserve water, and facilitate agriculture information. Space ventures will undergo improved modernization through the introduction of crop sprayers, precision gene editors, epigenetics, big data analytics, IoT, wind and photovoltaic smart energy, AI-enabled robotic applications, and wide-scale desalination technologies. Implementing digital farming systems in developing economies will help their sectors as 85 percent of the global population is set to live in developing countries by 2030. Automation will prove to be necessary since food scarcity is on the rise along with resource wastage. Control strategies such as the IoT, aerial imagery, machine learning, and artificial intelligence will boost production and prevent soil degradation. These advanced technologies are also able to alleviate such issues as plant disease detection, pesticide management, and water application. The introduction of the Internet of Things in the agricultural research world has started

关键词： Smart agriculture

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Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

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Tungsten 2025年第1期7卷 137-151页

作者： Geethukrishnan Onkar Apte Kiran Kumar Tadi Energy Electrodics and Electrocatalysis (EEE) Lab Department of Chemistry School of Advanced Sciences Vellore Institute of Technology School of Computer Science and Engineering Vellore Institute of Technology

The precise detection and measurement of dopamine（DA）,a crucial neurotransmitter in the human body,plays a significant role in diagnosing,preventing,and treating neurological diseases associated with its levels.A highly sensitive DA electrochemical sensor was constructed by combining molybdenum disulfide quantum dots（MSQDs） with multiwalled carbon nanotubes（MWCNTs）.The MSQDs were synthesized using the shear exfoliation *** sensors consist of MSQDs with Mo-S edge catalytic centers for the DA redox reaction,and MWCNTs amplify the sensor *** linearity of the sensor for the detection of DA was tested in the presence of ascorbic acid(AA,50 μmol·L-1) and uric acid(UA,200 μmol·L-1),and exhibited linearity from 2 to 966 μmol·L-1of DA with 0.097 μA(mol·L-1)-1sensitivity and a low limit of detection of0.6 μmol·L-1（the ratio between signal and noise,S/N=3）.Moreover,the sensitivity and selectivity of the sensor were also studied using *** is no increase in amperometric current after adding the most potentially interfering *** sensor was successfully applied to recover DA in human blood sera ***,machine learning algorithms were operated to aid in the near-precise detection of DA in the heterogeneous mixture containing AA and *** algorithms facilitate the identification and quantification of DA amidst coexisting interferents,including AA,that are commonly present in biological matrices.

关键词： Dopamine MoS_2 Biosensor Multiwalled carbon nanotubes Machine learning Complex mixture

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Fruit Quality Monitoring System using CNN 1

Fruit Quality Monitoring System using CNN

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ravikumar, B.N. Vidya, M.S. Gowda, Naveen Chandra NarendraBabu, C.R. Veena, H.N. Ramani, D. Roja Bms Institute of Technology and Management Department of Information Science and Engineering Bengaluru India Bms Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India School of Computer Science and Engineering Reva University Bengaluru India Sjb Institute of Technology Department of Computer Science and Engineering Bengaluru India New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529246

The grading of fruits relies on inspections, experiences, and observations, with a proposed system integrating machine learning techniques to assess fruit freshness. By analyzing 2D fruit portrayals based on shape and color, limitations arise as different fruits may share similar color, size, and shape values. To enhance accuracy, the system combines size, shape, and color analysis with Convolutional Neural Network (CNN) technology. Upon capturing the fruit image, properties like size, shape, and color are extracted, followed by CNN training and testing processes. The proposed work have implements UNet and En-UNet chosen over other architectures like ResNet or MobileNet for segmentation. This innovative approach utilizes CNN to extract comprehensive features, yielding promising results in fruit freshness detection with accuracy of 99% and loss rate of 0.0123. © 2025 IEEE.

关键词： Convolutional neural networks

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